We describe here sets of molecules that compute the state of the cell surface. The sets can be programmed to self-organize on cell surfaces displaying antibody-based targeting moieties in a way that results in unique tags (labels) displayed on the surfaces of arbitrarily narrow subpopulations of cells. The decision to display or not the tag will depend on antibodies that are present, i.e., on the multiple cell surface markers that define uniquely the targeted cells via their presence or absence. The resulting tag can be used to isolate in a single step the targeted cells in the presence of other subpopulations. This single-step method for isolation of cells based on multiple surface markers will have unique advantages over existing methods because of its scalability, efficacy, and mildness. We will study the scope and limitations of new technique on an example of therapeutically interesting subpopulations of lymphocytes, comparing our technique with existing methods and multistep protocols: Aim 1: in which the molecules will compute the presence of multiple markers on a cell surface. We will start with evaluation of up to three markers on CD4+CD25+ and CD4+CD25+CD154+ T- cells. We will then demonstrate the ability to isolate groups of cells based on the intensity of a marker, separating two fractions of NK cells (CD56dim and CD56high), developing sets of molecules with thresholding (cut off value) function. Aim 2: in which molecules will evaluate both the presence and absence of markers. After initial optimization on a model system, we will demonstrate the ability to isolate narrow subpopulations of conventional memory B-cells (CD19+CD21+CD27+IgD-) and recently characterized unconventional memory B-cells (CD19+CD21-/lowCD27-IgD-CD95+). Our reagents can be combined with any moiety used for targeting cell-surface markers with only minimal optimization. Thus, through our results we will introduce the biomedical community to a highly modular approach to isolating narrow populations of cells using of-the- shelf reagents. The method will have wide applications in basic science research, personalized medicine, and biomedical engineering.